# Clean data, filter, and remove unneeded columns.
sharps_boxes[, c("OBJECTID", "OBJECTID_1", "SITE_NAME", "SUB_NAME", "STATE")] <- list(NULL)
# Filter to Philadelphia
treatment_facilities <- treatment_facilities %>%
filter(`COUNTY` == "PHILADELPHIA")
treatment_facilities[, c("OBJECTID", "FACILITY_I", "GEOCODING_", "STREET_2", "CITY", "STATE", "TELEPHONE_")] <- list(NULL)
# Filter to active sites.
free_meal_sites <- free_meal_sites %>%
filter(`status` == "Active")
free_meal_sites[, c("objectid", "phone_numb", "temporary_", "site_key", "website", "hours_mon_", "hours_tues", "hours_wed_", "hours_thur", "hours_fri_", "hours_sat_", "hours_sa_1", "hours_sa_2", "hours_sun_", "email", "seasonally")] <- list(NULL)
substances_2024[, c("the_geom", "cartodb_id", "the_geom_webmercator", "objectid", "dc_dist", "psa", "dispatch_date_time", "dispatch_date", "dispatch_time", "hour", "dc_key", "ucr_general")] <- list(NULL)
philly_tracts[, c("STATEFP", "COUNTYFP", "GEOIDFQ", "NAME", "NAMELSAD", "STUSPS", "TRACTCE", "STATE_NAME", "LSAD", "TRACT")] <- list(NULL)
colnames(philly_tracts)[2] <- "county"